Internet websites, particularly search engines, have emerged as an attractive medium for advertisement of products, services, and the like. For example, when an Internet user conducts a search on a search engine, the search engine may provide the user search results as well as advertisements (hereinafter collectively referred to as recommendations) related to the search query. This gives the advertisers an opportunity to display their advertisements to the user when the user is actively seeking information on topics related to the advertised product or service.
Over the years, various advances in search technology have aimed to increase the relevance of recommendations to the user. This enhances the user experience. This also increases the likelihood of the user acting on the recommendations, such as clicking on a recommended advertisement or purchasing the recommended product or service. Personalized search and local search are two such notable advances.
Many popular Internet search engines provide personalized search functionality to customize the recommendations to the user's preferences. For example, if a user known to have an interest in wildlife conducts a search for “python”, a personalized search engine will provide recommendations related to python snakes. On the other hand, if another user known to have an interest in programming languages conducts the same search, the personalized search engine will provide recommendations related to the Python programming language. The users may explicitly define such preferences for the personalized search engine. Alternatively, the personalized search engine may learn the user's preferences by observing the user's online behavior over a period of time and/or through other means.
On the other hand, local search allows users to conduct geographically constrained searches. For example, if a user conducts a local search for a “restaurant”, the local search engine provides restaurants within, for example, the user's city or the user's locality. Local search has made the Internet a viable advertising medium for businesses that have a predominantly local clientele. Further, and perhaps more interestingly, local search has increasingly drawn users to conduct Internet searches for day-to-day activities such as finding eating establishments, movie shows, product showrooms, shopping options, banks, automated teller machines, bookstores, flower shops, real estate agents, and the like. Many known local search solutions use the user's current location, often derived by locating the user's mobile device using technologies such as Global Positioning System (GPS), cellular tower triangulation, and the like, to define the geographical constraints of the search.
Some known solutions offer a combination of personalized search and local search, that is, they provide recommendations that are geographically constrained as well as customized according to the user's preferences. For example, if a user known to like Chinese food searches for a “restaurant”, the search engine gives a higher priority to Chinese restaurants in the user's vicinity vis-à-vis other restaurants. Users are increasingly using such solutions to obtain recommendations for their day-to-day activities.
However, in practice, users often seek recommendations for activities in which two or more people participate. For example, a user may want recommendations for a restaurant to meet two friends over lunch. Most present solutions provide results personalized to the user who conducted the search, and in most cases entirely neglect the preferences and/or the current location of other users participating in the activity. This reduces the relevance of the recommendations and therefore the likelihood of the user acting upon the recommendations. Therefore, there is a need for a more effective technique of providing recommendations for activities involving more than one user.